n Expert opinion — Liquidnet

remained stable though in
reality both counterparties
are subject to exogenous
factors such as volatility.

In the second example,
Figure 2, the traditional
algo continues to drip feed
sell liquidity to the market
over an extended period
but faces off against a
liquidity seeking algo. The
latter is not just limited to
a scheduled participation
in lit markets but is able
to opportunistically draw
liquidity from the market
across multiple venues. The
liquidity seeking buy algo
completes the order more
quickly, with low market
impact and low risk. The
scheduled sell order, on the
other hand, is left feeding stock into a market over
an extended period. The offsetting buyer no longer
present, the balance is now disrupted resulting in
extended timing risk and market impact.

An even more complex case can be made witha traditional schedule algorithm trading in both litand dark venues against a smarter liquidity-seekingalgorithm:Here the smart liquidity seeking algorithm trad-ing a buy order reduces its lit market participationto take advantage of the dark liquidity streak. Theparticipation sell algorithm, as in Figure 2, continuestrading in lit, disrupting the lit market balance andpushing the price down. For the seller, the adverseprice move coincides with trading in dark venueswhich appear more and more “toxic” as the tradermore often faces smart liquidity seeking algorithmson the opposite side of the trade.

In hindsight, one could argue that in Figure 2 and3 the buy algorithm could have traded at an evenbetter price if it reduced its participation even more.We must remember, however, that such 20/20 visionis not available even to a smartest algorithm. Everyalgorithmic trade faces several conflicting objectives: tocontain market impact on one hand, and exposure tovolatility and timing risk on the other hand, for exam-ple. In pursuit of this balance intelligent algorithmsmust act on what is known now and may forgo uncer-tain opportunities to achieve their main objectives.Our preliminary transaction cost analysis acrossour algorithmic trading platform demonstrates anadvantage of using broad-based lit & dark liquid-ity seeking strategies vs. scheduled algorithms whenmeasured on an implementation shortfall basis. Fororders of similar characteristics executed by a liquidityseeking algorithm versus a standard POV algorithm:a) Timing risk measured as the width of the perfor-mance distribution in terms of daily volatility isreduced by more than 30%;b) Negative tail of the performance distribution(worst 1/5 of all orders) is shorter by more than20% for comparable orders2.These results may align well with many partici-pants’ intuition regarding the advantages of tradingBuy 350 shares, liquidity-seeking algorithmSell 350 shares, scheduled algorithm80

S
ha
re
s

P
ri
ce

TimeBuyer – Lit Buyer – Dark Seller Price

FIGURE 2. LIQUIDITY SEEKING ALGORITHM VS LIT SCHEDULED
ALGORITHM

Source: Liquidnet

2. Based on internal Liquidnet transaction cost analysis data since the
beginning of 2016 through July 2016.